Curriculum Vitae

I seek an academic community in which I can grow and improve as a faculty member through my contributions in teaching, research, and service. My research involves two main lines of investigation: in the first I use the power of computation to explore how natural evolution produces complex traits, and in the second I use my improved understanding of these natural mechanisms to design next-generation evolutionary algorithms that can solve real-world problems. I am interested in computer-science education and exploring both old and new methods to meet the unique challenges involved in teaching the type of problem solving and abstract thinking that is required to learn computer programming. This includes exploring new initiatives to attract and retain women and ethnic groups currently under-represented in computer-science.

Instructor5/2008 - 12/2008Developed course materials (including lectures, slides, examples, exams, and homework) and instructed CSE 231 (introduction to OOP using Python). Gained experience teaching in diverse environments, one semester of an accelerated class with 25 students, and one semester with a large lecture of 100+ students

Mentor to undergraduate researchers5/2005 - presentJason Rapei: researched the ability of individuals to sense environmental cues and use this information to evolve plastic behavior.Eric Mueller: explored solving multi-objective optimization problems in an ecological EA that used limited resources to maintain population diversity.Meryl Mabin: Currently studying the evolution of altruism and cooperation in digital populations.

Altruism in artificial life: Studied the mechanisms through which altruism can evolve, specifically how kin-selection may cause an individual to sacrifice itself for its relatives.

Automatic parameter setting in genetic algorithms: Researched using a meta-GA to automatically set parameters in GAs on the fly, allowing for less knowledgable users to take advantage of genetic algorithm.

Evolving rational agents in a P2P network: Researched using a GA to evolve strategies for rational agents in a P2P resource sharing system. Evolving distributed problem solving in artificial life: Exploring using a market-based economy in a population of evolving individuals to allow them to subdivide a problem and share individual components in order to build the final solution.Using limited resources to maintain population diversity: Developing an Eco-EA which uses the nature-inspired mechanism of limited resources to promote and maintain diversity in an evolving population. Studying how using this Eco-EA can improve the evolution of solutions to complex problems, and can be used to solve multi-objective problems. Specifically applying the Eco-EA to the evolution of behavioral UML models to solve complex software-engineering problems.